A vision-based method for spatial and temporal tracking of individual whitecaps from breaking ocean waves

Sea surface wave breaking is the dominant mechanism behind ocean wave energy dissipation. During the breaking process, wave energy is converted into turbulent kinetic energy, and if breaking is significantly energetic, entrains air which facilitates air-sea gas transfer and scatters light to create the signature whitecap. Exploiting the broadband scattering of light by the surface whitecaps, this study develops an algorithm for Automated Whitecap Detection And Tracking (AWDAT) from fixed image systems, in order to detect and track individual air-entraining surface breaking waves. AWDAT builds on previous whitecap identification studies through introduction of new image processing and computer vision techniques that handle temporal developments of whitecaps and complex behaviours of whitecap foam such as splitting and merging. Taking advantage of the self-similar behaviour observed for whitecap surface foam area evolution [1], we teach a learning based model to filter tracked whitecaps based on the AWDAT-measured foam area, breaking speed and direction time series. The algorithm is tested on three different image data sets to assess its performance with different camera systems in different geographic locations - The Adriatic, Black and Yellow Seas. Applications of AWDAT are demonstrated by aggregating whitecap statistics from geometric, kinematic and dynamic measurements of individual breaking waves which are then evaluated within the [2] volume-time-integral (VTI) method and Phillips (1985) Λ(c) spectral framework.